GFDL Lunchtime Seminar

Wed, Jan 15, 2020, 12:00 pm to 1:00 pm

The seasonal prediction of Arctic sea ice, especially in the summertime, is vital to human activities and environment protections. The lack of constraint on sea ice initial conditions is one of the major hurdles for predicting summer Arctic sea ice several months ahead of time. This study exploits data assimilation (DA) to generate a better sea ice reanalysis and study the potential benefits of a more accurate initial condition. The GFDL Sea-Ice Simulator version 2 (SIS2) is coupled with the GFDL Modular Ocean Model version 6 (MOM6) and forced by a single atmosphere from the JRA-55 reanalysis. We link SIS2 and the data assimilation research testbed (DART) to conduct DA experiments. The sea ice concentration (SIC) observations from NSIDC are assimilated every 5 days from 1982 to 2017 through the Ensemble adjustment Kalman filter (EAKF). Before applying DA, we restore the sea surface temperature (SST) to the daily Optimum Interpolation Sea Surface Temperature (OISST), which improves our model background of SIC and also ameliorates an over-shooting problem arisen from SIC DA. We test the influences of different localization cutoffs, observation errors, and DA frequencies on the results. Our best DA experiment increases the September pan-Arctic sea ice extent (SIE) correlation and better reproduces the decreasing trend of pan-Arctic September SIE. Performances of SIC DA at regional scales are also discussed in our study. At the end of the talk, we show that the improved initial conditions of SIC and SIE have prospects for advancing short-lead time predictions of the summer Arctic sea ice.

Location: 
Smagorinsky Seminar Room 209
Speaker(s): 

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Location: Smagorinsky Seminar Room 209